1.1 Looking at the data with Lares

tibble [1,386 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ date         : Date[1:1386], format: "2000-04-01" "2000-04-01" ...
 $ iso_a3       : chr [1:1386] "ARG" "AUS" "BRA" "CAN" ...
 $ currency_code: chr [1:1386] "ARS" "AUD" "BRL" "CAD" ...
 $ name         : chr [1:1386] "Argentina" "Australia" "Brazil" "Canada" ...
 $ local_price  : num [1:1386] 2.5 2.59 2.95 2.85 5.9 ...
 $ dollar_ex    : num [1:1386] 1 1.68 1.79 1.47 1.7 ...
 $ dollar_price : num [1:1386] 2.5 1.54 1.65 1.94 3.47 ...
 $ usd_raw      : num [1:1386] -0.00398 -0.38579 -0.34341 -0.22758 0.3827 ...
 $ eur_raw      : num [1:1386] 0.0501 -0.3525 -0.3078 -0.1857 0.4577 ...
 $ gbp_raw      : num [1:1386] -0.167 -0.486 -0.451 -0.354 0.156 ...
 $ jpy_raw      : num [1:1386] -0.0986 -0.4442 -0.4058 -0.301 0.2513 ...
 $ cny_raw      : num [1:1386] 1.091 0.289 0.378 0.622 1.903 ...
 $ gdp_dollar   : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ adj_price    : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ usd_adjusted : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ eur_adjusted : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ gbp_adjusted : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ jpy_adjusted : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 $ cny_adjusted : num [1:1386] NA NA NA NA NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   date = col_date(format = ""),
  ..   iso_a3 = col_character(),
  ..   currency_code = col_character(),
  ..   name = col_character(),
  ..   local_price = col_double(),
  ..   dollar_ex = col_double(),
  ..   dollar_price = col_double(),
  ..   usd_raw = col_double(),
  ..   eur_raw = col_double(),
  ..   gbp_raw = col_double(),
  ..   jpy_raw = col_double(),
  ..   cny_raw = col_double(),
  ..   gdp_dollar = col_double(),
  ..   adj_price = col_double(),
  ..   usd_adjusted = col_double(),
  ..   eur_adjusted = col_double(),
  ..   gbp_adjusted = col_double(),
  ..   jpy_adjusted = col_double(),
  ..   cny_adjusted = col_double()
  .. )

1.2 Plotting the data myself

Looks like Turkey messed up so I filter it out in the next plot, Vietnam and Indonesia have similarly ridiculous numbers so I also filter those in the next plot, after that I decided the best way to do this was grouping the plots since there is no level at which all will be plotted reasonably.